In [1]:
import os

## Set directory
os.chdir('/hpc/group/pbenfeylab/CheWei/CW_data/genesys')

import networkx as nx
from genesys_evaluate_v1 import *
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings
# Suppress all warning messages
warnings.filterwarnings("ignore", category=DeprecationWarning)
In [2]:
## Conda Env pytorch-gpu on DCC
print(torch.__version__)
print(sc.__version__) 

1.13.0.post200
1.9.1
In [3]:
## Genes considered/used (shared among samples) 
gene_list = pd.read_csv('./gene_list_1108.csv')

Load Data¶

In [4]:
with open("./genesys_root_data.pkl", 'rb') as file_handle:
    data = pickle.load(file_handle)
    
batch_size = 2000
dataset = Root_Dataset(data['X_test'], data['y_test'])
loader = DataLoader(dataset,
                         batch_size = batch_size,
                         shuffle = True, drop_last=True)
In [5]:
input_size = data['X_train'].shape[1]
## 10 cell types 
output_size = 10
embedding_dim = 256
hidden_dim = 256
n_layers = 2
device = "cpu"
path = "./"

Load trained GeneSys model¶

In [6]:
model = ClassifierLSTM(input_size, output_size, embedding_dim, hidden_dim, n_layers).to(device)
model.load_state_dict(torch.load(path+"best_ALL_1130_continue.pth", map_location=torch.device('cpu')))
model = model
model.eval()
Out[6]:
ClassifierLSTM(
  (fc1): Sequential(
    (0): Linear(in_features=17513, out_features=256, bias=True)
    (1): Dropout(p=0.2, inplace=False)
    (2): GaussianNoise()
  )
  (fc): Sequential(
    (0): ReLU()
    (1): Linear(in_features=512, out_features=512, bias=True)
    (2): ReLU()
    (3): Linear(in_features=512, out_features=10, bias=True)
  )
  (lstm): LSTM(256, 256, num_layers=2, batch_first=True, dropout=0.2, bidirectional=True)
  (dropout): Dropout(p=0.2, inplace=False)
  (b_to_z): DBlock(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=512, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (bz2_infer_z1): DBlock(
    (fc1): Linear(in_features=1024, out_features=256, bias=True)
    (fc2): Linear(in_features=1024, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (z1_to_z2): DBlock(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=512, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (z_to_x): Decoder(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=256, out_features=256, bias=True)
    (fc3): Linear(in_features=256, out_features=17513, bias=True)
  )
)
In [7]:
classes = ['Columella', 'Lateral Root Cap', 'Phloem', 'Xylem', 'Procambium', 'Pericycle', 'Endodermis', 'Cortex', 'Atrichoblast', 'Trichoblast']
class2num = {c: i for (i, c) in enumerate(classes)}
num2class = {i: c for (i, c) in enumerate(classes)}
In [8]:
cts = ['Atrichoblast','Trichoblast','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Lateral Root Cap','Columella']
ctw = np.zeros((len(cts), 17513, 17513))
## number of cells sampled from the atlas
batch_size = 2000
In [9]:
## GRN for the transition t1 to t3
for ct in cts:
    print(ct)
    cws = np.zeros((len(loader), 17513, 17513))
    with torch.no_grad():
        for i, sample in enumerate(loader):
            x = sample['x'].to(device)
            y = sample['y'].to(device)
            y_label = [num2class[i] for i in y.tolist()]
            
            pred_h = model.init_hidden(batch_size)
            tfrom = model.generate_next(x, pred_h, 0).to('cpu').detach().numpy()
            cfrom = tfrom[np.where(np.array(y_label)==ct)[0],:]
            
            pred_h = model.init_hidden(batch_size)
            tto = model.generate_next(x, pred_h, 2).to('cpu').detach().numpy()   
            cto = tto[np.where(np.array(y_label)==ct)[0],:]
            
            cw = torch.linalg.lstsq(torch.tensor(cfrom), torch.tensor(cto)).solution.detach().numpy()
            cws[i] = cw
    
    ## Calculate mean across number of repeats
    cwm = np.mean(cws, axis=0)
    ctw[cts.index(ct)] = cwm

Atrichoblast
Trichoblast
Cortex
Endodermis
Pericycle
Procambium
Xylem
Phloem
Lateral Root Cap
Columella
In [10]:
# Save the array to disk
np.save('genesys_ctw_t1-t3.npy', ctw)
In [11]:
ctw = np.load('genesys_ctw_t1-t3.npy')
In [12]:
## Calculate z-scores
ctw_z = np.zeros((len(cts), 17513, 17513))
for i in range(len(cts)):
    ctw_z[i] = (ctw[i] - np.mean(ctw[i])) / np.std(ctw[i])
In [13]:
## Filtering based on z-scores (with no weights)
ctw_f = np.zeros((len(cts), 17513, 17513))
## z-score threshold (keep values > mean + threshold*std)
threshold=3
for i in range(len(cts)):
    ctw_f[i] = np.abs(ctw_z[i]) > threshold

Load TFs list¶

In [14]:
wanted_TFs = pd.read_csv("./Kay_TF_thalemine_annotations.csv")
In [15]:
## Make TF names unique and assign preferred names
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G33880"]="WOX9"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G45160"]="SCL27"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G04410"]="NAC78"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G29035"]="ORS1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02540"]="ZHD3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G16500"]="IAA26"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G09740"]="HAG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G24660"]="ZHD2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G46880"]="HDG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G28420"]="RLT1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G14580"]="BLJ"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G45260"]="BIB"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02070"]="RVN"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G28160"]="FIT"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G68360"]="GIS3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G20640"]="NLP4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G05550"]="VFP5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G59470"]="FRF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G15150"]="HAT7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G14750"]="WER"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G75710"]="BRON"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G74500"]="TMO7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G12646"]="RITF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G48100"]="ARR5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G16141"]="GATA17L"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G65640"]="NFL"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G62700"]="VND5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G36160"]="VND2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G66300"]="VND3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G12260"]="VND4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G62380"]="VND6"
In [16]:
pd.Series(wanted_TFs['Name']).value_counts().head(5)
Out[16]:
NAC001    1
PRE5      1
MYB118    1
MYB21     1
MYB0      1
Name: Name, dtype: int64

Network analysis¶

In [17]:
TFidx = []
for i in wanted_TFs['GeneID']:
    if i in gene_list['features'].tolist():
        TFidx.append(np.where(gene_list['features']==i)[0][0])

TFidx = np.sort(np.array(TFidx))
In [18]:
def network(i):
    ## No weights
    adj_nw = ctw_f[i]
    ## Weighted
    adj = ctw[i]*ctw_f[i]
    ## TF only
    adj = adj[np.ix_(TFidx,TFidx)]
    adj_nw = adj_nw[np.ix_(TFidx,TFidx)]
    
    ## Remove no connect 
    regidx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[0]).value_counts().index[pd.Series(np.where(adj_nw==True)[0]).value_counts()>=1]))
    taridx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[1]).value_counts().index[pd.Series(np.where(adj_nw==True)[1]).value_counts()>=1]))
    ## Reciprocol
    keepidx = np.sort(np.array(list(set(regidx).intersection(taridx))))
    #keepidx = np.sort(np.array(list(set(regidx).union(taridx))))
    
    TFID = np.array(gene_list['features'][TFidx])[keepidx].tolist()
    ## TF name to keep
    TFname = []
    for i in np.array(gene_list['features'][TFidx])[keepidx]:
        TFname.append(wanted_TFs['Name'][np.where(wanted_TFs['GeneID']==i)[0][0]])
        
    adj = adj[np.ix_(keepidx,keepidx)]
    
    # Create a NetworkX graph for non-directed edges
    G = nx.Graph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(i, j, weight=abs(weight), distance=1/abs(weight))
            

    ## Measures the extent to which how close a node is to all other nodes in the network, considering the shortest paths or geodesic distances between nodes
    closeness_centrality = nx.closeness_centrality(G, distance='distance')
    ## Measures the extent to which a node that are not only well-connected but also connected to other well-connected nodes.
    eigenvector_centrality = nx.eigenvector_centrality(G)
    
    # Create a NetworkX graph for diected edges
    G = nx.MultiDiGraph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(i, j, weight=weight)
    
    ## Measures the number of connections (edges) each node has
    degree_centrality = nx.degree_centrality(G)
    # Calculate outgoing centrality
    out_centrality = nx.out_degree_centrality(G)
    # Calculate incoming centrality
    in_centrality = nx.in_degree_centrality(G)
    ## Measures the extent to which a node lies on the shortest paths between other nodes.
    betweenness_centrality = nx.betweenness_centrality(G, weight='weight')
    
    ## Non_Reciprocal Out centrality
    # Visualize the graph
    pos = nx.spring_layout(G)  # Positions of the nodes
    
    # Node colors based on weighted betweenness centrality
    node_colors = [out_centrality[node] for node in G.nodes()]
    
    # Node sizes based on weighted betweenness centrality
    node_sizes = [out_centrality[node] * 1000 for node in G.nodes()]

    # Get the edge weights as a dictionary
    edge_weights = nx.get_edge_attributes(G, 'weight')
    edge_colors = ['red' if weight > 0 else 'blue' for (_, _, weight) in G.edges(data='weight')]
    
    # Scale the edge weights to desired linewidths
    max_weight = max(edge_weights.values())
    edge_widths = [float(edge_weights[edge]) / max_weight for edge in G.edges]
    
    # Draw the graph
    nx.draw(G, pos=pos, node_color=node_colors, node_size=node_sizes, with_labels=False, width=edge_widths, edge_color=edge_colors)
    # Add node labels
    labels = {node: G.nodes[node]['name'] for node in G.nodes}
    nx.draw_networkx_labels(G, pos=pos, labels=labels, font_size=8)
    
    # Add a colorbar to show the weighted betweenness centrality color mapping
    sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=min(node_colors), vmax=max(node_colors)))
    sm.set_array([])
    plt.colorbar(sm)
    
    # Show the plot
    plt.show()
    
    dc = pd.DataFrame.from_dict(degree_centrality, orient='index', columns=['degree_centrality'])
    oc = pd.DataFrame.from_dict(out_centrality, orient='index', columns=['out_centrality'])
    ic = pd.DataFrame.from_dict(in_centrality, orient='index', columns=['in_centrality'])
    bc = pd.DataFrame.from_dict(betweenness_centrality, orient='index', columns=['betweenness_centrality'])
    cc = pd.DataFrame.from_dict(closeness_centrality, orient='index', columns=['closeness_centrality'])
    ec = pd.DataFrame.from_dict(eigenvector_centrality, orient='index', columns=['eigenvector_centrality'])
    df = pd.concat([dc,oc,ic,bc,cc,ec], axis=1)
    df.index =TFname
    df = df.sort_values('betweenness_centrality', ascending=False)
    
    return(df)
In [19]:
atri = network(0)
In [20]:
tri = network(1)
In [21]:
cor = network(2)
In [22]:
end = network(3)
In [23]:
per = network(4)
In [24]:
pro = network(5)
In [25]:
xyl = network(6)
In [26]:
phl = network(7)
In [27]:
lrc = network(8)
In [28]:
col = network(9)
In [29]:
atri.columns = ['atri_degree_centrality','atri_out_centrality','atri_in_centrality','atri_betweenness_centrality','atri_closeness_centrality','atri_eigenvector_centrality']
tri.columns = ['tri_degree_centrality','tri_out_centrality','tri_in_centrality','tri_betweenness_centrality','tri_closeness_centrality','tri_eigenvector_centrality']
cor.columns = ['cor_degree_centrality','cor_out_centrality','cor_in_centrality','cor_betweenness_centrality','cor_closeness_centrality','cor_eigenvector_centrality']
end.columns = ['end_degree_centrality','end_out_centrality','end_in_centrality','end_betweenness_centrality','end_closeness_centrality','end_eigenvector_centrality']
per.columns = ['per_degree_centrality','per_out_centrality','per_in_centrality','per_betweenness_centrality','per_closeness_centrality','per_eigenvector_centrality']
pro.columns = ['pro_degree_centrality','pro_out_centrality','pro_in_centrality','pro_betweenness_centrality','pro_closeness_centrality','pro_eigenvector_centrality']
xyl.columns = ['xyl_degree_centrality','xyl_out_centrality','xyl_in_centrality','xyl_betweenness_centrality','xyl_closeness_centrality','xyl_eigenvector_centrality']
phl.columns = ['phl_degree_centrality','phl_out_centrality','phl_in_centrality','phl_betweenness_centrality','phl_closeness_centrality','phl_eigenvector_centrality']
lrc.columns = ['lrc_degree_centrality','lrc_out_centrality','lrc_in_centrality','lrc_betweenness_centrality','lrc_closeness_centrality','lrc_eigenvector_centrality']
col.columns = ['col_degree_centrality','col_out_centrality','col_in_centrality','col_betweenness_centrality','col_closeness_centrality','col_eigenvector_centrality']
In [30]:
## Indentify main regulators in each net work
tff = []
tff = tff + atri[atri['atri_betweenness_centrality']>0].index.tolist()
tff = tff + tri[tri['tri_betweenness_centrality']>0].index.tolist()
tff = tff + lrc[lrc['lrc_betweenness_centrality']>0].index.tolist()
tff = tff + cor[cor['cor_betweenness_centrality']>0].index.tolist()
tff = tff + end[end['end_betweenness_centrality']>0].index.tolist()
tff = tff + per[per['per_betweenness_centrality']>0].index.tolist()
tff = tff + pro[pro['pro_betweenness_centrality']>0].index.tolist()
tff = tff + xyl[xyl['xyl_betweenness_centrality']>0].index.tolist()
tff = tff + phl[phl['phl_betweenness_centrality']>0].index.tolist()
tff = tff + col[col['col_betweenness_centrality']>0].index.tolist()
tf_occurance = pd.DataFrame(pd.Series(tff).value_counts(), columns=['tf_occurance'])
tf_spec = pd.concat([tf_occurance, atri, tri, lrc, cor, end, per, pro, xyl, phl, col], axis=1)
tf_spec = tf_spec.fillna(0)
In [31]:
## Epidermis (atri, tri, lrc)
celltype1='atri'
celltype2='tri'
celltype3='lrc'
ts = tf_spec[tf_spec['tf_occurance']==3][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==9].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[31]:
atri_betweenness_centrality tri_betweenness_centrality lrc_betweenness_centrality atri_out_centrality tri_out_centrality lrc_out_centrality atri_in_centrality tri_in_centrality lrc_in_centrality centrality_count centrality_sum
WRKY9 0.948657 0.615222 0.932734 0.184314 0.235294 0.226682 0.500000 0.135027 0.316411 9 13.094342
ATS 0.932748 0.488213 0.852591 0.201961 0.098930 0.110980 0.562745 0.140374 0.329398 9 12.717940
GL2 0.918236 0.703726 0.472882 0.349020 0.594920 0.031877 0.411765 0.108289 0.020071 9 12.610785
LBD15 0.860780 0.334223 0.173497 0.158824 0.056150 0.550177 0.343137 0.133690 0.351830 9 11.962307
AT1G36060 0.550021 0.053925 0.459932 0.431373 0.247326 0.318772 0.162745 0.046791 0.232586 9 11.503472
GATA17 0.895543 0.002348 0.299921 0.352941 0.122995 0.383707 0.247059 0.010695 0.152302 9 11.467511
TTG2 0.851296 0.436699 0.085448 0.225490 0.165775 0.008264 0.331373 0.088235 0.011806 9 11.204388
ANL2 0.762518 0.015663 0.565964 0.107843 0.040107 0.106257 0.145098 0.084225 0.247934 9 11.075609
BNQ3 0.852918 0.530122 0.049549 0.070588 0.110963 0.011806 0.239216 0.052139 0.011806 9 10.929108
CRF4 0.896868 0.220560 0.048572 0.249020 0.080214 0.036600 0.290196 0.048128 0.017710 9 10.887867
WRKY27 0.715563 0.024236 0.396954 0.139216 0.104278 0.109799 0.174510 0.033422 0.055490 9 10.753468
FIT 0.571301 0.155044 0.189824 0.017647 0.025401 0.025974 0.392157 0.112299 0.044864 9 10.534512
WRI1 0.767811 0.000598 0.001059 0.150980 0.048128 0.074380 0.178431 0.010695 0.053129 9 10.285212
AT5G44260 0.005520 0.194878 0.007405 0.100000 0.016043 0.031877 0.072549 0.120321 0.062574 9 9.611166
WRKY72 0.001606 0.088146 0.030056 0.062745 0.144385 0.070838 0.062745 0.025401 0.024793 9 9.510716
In [32]:
## atri, tri
celltype1='atri'
celltype2='tri'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[32]:
atri_betweenness_centrality tri_betweenness_centrality atri_out_centrality tri_out_centrality atri_in_centrality tri_in_centrality centrality_count centrality_sum
ATMYC1 0.486764 0.744150 0.478431 0.101604 0.052941 0.296791 6 8.160682
RHD6 0.633746 0.623598 0.090196 0.135027 0.047059 0.211230 6 7.740855
TRY 0.792453 0.237884 0.090196 0.233957 0.221569 0.037433 6 7.613492
IAA14 0.863658 0.296280 0.029412 0.143048 0.192157 0.076203 6 7.600758
EGL3 0.449728 0.348768 0.094118 0.040107 0.031373 0.101604 6 7.065697
HMGB5 0.523895 0.000002 0.096078 0.042781 0.188235 0.012032 6 6.863024
AT5G12980 0.001957 0.021781 0.052941 0.058824 0.160784 0.017380 6 6.313666
OFP18 0.057221 0.001510 0.027451 0.029412 0.127451 0.022727 6 6.265772
In [33]:
## Atrichoblast specific
celltype = 'atri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[33]:
atri_betweenness_centrality atri_out_centrality atri_in_centrality centrality_count centrality_sum
NF-YC13 0.273319 0.050980 0.062745 3 3.387045
FVE 0.118510 0.121569 0.100000 3 3.340079
SUVR1 0.091806 0.141176 0.017647 3 3.250630
PS1 0.077106 0.135294 0.027451 3 3.239851
HDG7 0.074001 0.098039 0.064706 3 3.236746
GRF3 0.054644 0.080392 0.064706 3 3.199742
OFP15 0.018217 0.149020 0.105882 3 3.273119
HDG1 0.010139 0.164706 0.043137 3 3.217982
AT3G56570 0.008552 0.233333 0.050980 3 3.292866
PRMT3 0.001876 0.088235 0.101961 3 3.192072
pde191 0.001063 0.050980 0.076471 3 3.128514
CRF10 0.000339 0.186275 0.049020 3 3.235633
RSZ22a 0.000050 0.098039 0.031373 3 3.129462
IAA20 0.000004 0.164706 0.058824 3 3.223533
CHR1 0.000004 0.121569 0.060784 3 3.182357
In [34]:
## Trichoblast specific
celltype = 'tri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[34]:
tri_betweenness_centrality tri_out_centrality tri_in_centrality centrality_count centrality_sum
TAFII15 0.528436 0.040107 0.028075 3 3.596618
AT2G37120 0.439641 0.049465 0.176471 3 3.665577
RSL1 0.398866 0.216578 0.117647 3 3.733091
WRKY75 0.303063 0.010695 0.224599 3 3.538357
AT4G39160 0.288006 0.009358 0.171123 3 3.468487
MED6 0.278431 0.065508 0.050802 3 3.394741
ZF1 0.158022 0.040107 0.042781 3 3.240910
LRL3 0.126848 0.001337 0.164439 3 3.292623
WRKY42 0.039010 0.016043 0.021390 3 3.076443
RSL4 0.038342 0.006684 0.250000 3 3.295027
RL6 0.003667 0.026738 0.098930 3 3.129336
GL3 0.001376 0.032086 0.026738 3 3.060200
AGL87 0.000336 0.086898 0.010695 3 3.097930
In [35]:
## LRC specific
celltype = 'lrc'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[35]:
lrc_betweenness_centrality lrc_out_centrality lrc_in_centrality centrality_count centrality_sum
TFIIIA 0.632137 0.054309 0.059032 3 3.745478
AT1G70000 0.380033 0.279811 0.059032 3 3.718876
PPD1 0.242391 0.024793 0.042503 3 3.309687
LBD41 0.219469 0.044864 0.023613 3 3.287946
WRKY35 0.084745 0.016529 0.034238 3 3.135512
DAR7 0.062212 0.008264 0.022432 3 3.092909
AT3G05670 0.055438 0.009445 0.018890 3 3.083774
AT5G64220 0.039459 0.038961 0.029516 3 3.107936
GRF2 0.037020 0.070838 0.037780 3 3.145638
AT1G19000 0.033417 0.090909 0.022432 3 3.146758
RDUF1 0.013350 0.030697 0.077922 3 3.121969
WRKY7 0.012956 0.113341 0.028335 3 3.154633
AT2G39020 0.011201 0.021251 0.057851 3 3.090303
AT3G52250 0.010556 0.014168 0.029516 3 3.054240
OFP6 0.008905 0.009445 0.077922 3 3.096272
CSDP1 0.007856 0.144038 0.079103 3 3.230996
AT5G66770 0.005547 0.048406 0.016529 3 3.070482
SDG4 0.005130 0.123967 0.047226 3 3.176322
LDL1 0.004872 0.029516 0.020071 3 3.054459
BIM2 0.004435 0.038961 0.020071 3 3.063467
AT1G21000 0.003129 0.018890 0.037780 3 3.059799
AT2G44430 0.002458 0.036600 0.016529 3 3.055586
ZFP6 0.001345 0.020071 0.055490 3 3.076906
FRS1 0.001235 0.029516 0.018890 3 3.049641
LOL2 0.001181 0.012987 0.020071 3 3.034238
AT3G02890 0.001181 0.009445 0.094451 3 3.105077
RGL1 0.000751 0.008264 0.024793 3 3.033809
AT2G35910 0.000720 0.027155 0.037780 3 3.065655
AT5G51910 0.000596 0.049587 0.041322 3 3.091505
ATXR5 0.000417 0.053129 0.034238 3 3.087784
IDD2 0.000234 0.085006 0.021251 3 3.106492
PC-MYB1 0.000198 0.024793 0.021251 3 3.046243
CHR17 0.000091 0.069658 0.061393 3 3.131141
AT3G20010 0.000077 0.044864 0.040142 3 3.085083
AT5G19490 0.000066 0.024793 0.023613 3 3.048472
AT5G08630 0.000066 0.020071 0.016529 3 3.036665
JMJ18 0.000061 0.037780 0.025974 3 3.063816
SCL8 0.000024 0.015348 0.033058 3 3.048430
In [36]:
## Columella specific
celltype = 'col'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[36]:
col_betweenness_centrality col_out_centrality col_in_centrality centrality_count centrality_sum
NAC042 0.853618 0.009249 0.205780 3 4.068647
PIF3 0.152002 0.094798 0.143353 3 3.390152
CRF11 0.079147 0.048555 0.038150 3 3.165852
SPL14 0.022550 0.171098 0.107514 3 3.301163
COL5 0.020871 0.106358 0.316763 3 3.443992
TLP7 0.017514 0.270520 0.126012 3 3.414045
TGA7 0.015615 0.093642 0.077457 3 3.186713
NAC052 0.008074 0.020809 0.154913 3 3.183796
FBH4 0.005778 0.024277 0.092486 3 3.122541
AT1G21580 0.005743 0.041618 0.098266 3 3.145627
AT3G06410 0.004494 0.320231 0.082081 3 3.406807
SPL11 0.002222 0.001156 0.004624 3 3.008003
LUG 0.002073 0.090173 0.146821 3 3.239067
AT1G77570 0.001156 0.006936 0.023121 3 3.031214
TRFL3 0.001128 0.001156 0.046243 3 3.048527
EICBP.B 0.001057 0.223121 0.084393 3 3.308572
AT1G02080 0.000903 0.208092 0.093642 3 3.302637
SYD 0.000860 0.092486 0.159538 3 3.252883
BRM 0.000765 0.228902 0.166474 3 3.396141
CHR4 0.000613 0.180347 0.115607 3 3.296567
SR1 0.000167 0.089017 0.166474 3 3.255659
AT2G02160 0.000005 0.198844 0.054335 3 3.253185
HSL1 0.000003 0.057803 0.127168 3 3.184974
TTG1 0.000003 0.077457 0.039306 3 3.116766
IDD1 0.000001 0.047399 0.160694 3 3.208094
In [37]:
## Ground tissue
celltype1='cor'
celltype2='end'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[37]:
cor_betweenness_centrality end_betweenness_centrality cor_out_centrality end_out_centrality cor_in_centrality end_in_centrality centrality_count centrality_sum
MYB36 0.787439 0.118912 0.090395 0.215633 0.279661 1.002695 6 8.494736
MYB3 0.519150 0.059532 0.073446 0.212938 0.096045 0.975741 6 7.936853
MYB74 0.306885 0.075297 0.002825 0.072776 0.180791 0.943396 6 7.581970
MYB68 0.225300 0.078866 0.039548 0.172507 0.064972 0.905660 6 7.486854
BLJ 0.040156 0.362024 0.019774 0.086253 0.042373 0.770889 6 7.321470
AT1G72210 0.018598 0.000197 0.104520 0.239892 0.285311 0.056604 6 6.705121
AT5G59450 0.002369 0.000845 0.124294 0.129380 0.011299 0.358491 6 6.626678
APRR2 0.006850 0.000146 0.110169 0.169811 0.050847 0.035040 6 6.372864
SCL3 0.000016 0.000087 0.014124 0.059299 0.019774 0.226415 6 6.319716
RR3 0.000008 0.000175 0.087571 0.121294 0.042373 0.013477 6 6.264897
ARR4 0.001745 0.000029 0.042373 0.113208 0.036723 0.010782 6 6.204859
EPR1 0.004545 0.000838 0.045198 0.075472 0.022599 0.002695 6 6.151347
In [38]:
## Cortex specific
celltype = 'cor'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[38]:
cor_betweenness_centrality cor_out_centrality cor_in_centrality centrality_count centrality_sum
AT2G38300 0.001584 0.002825 0.491525 3 3.495935
EIL1 0.000120 0.025424 0.124294 3 3.149838
In [39]:
## Endodermis specific
celltype = 'end'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[39]:
end_betweenness_centrality end_out_centrality end_in_centrality centrality_count centrality_sum
BIB 0.240286 0.061995 0.328841 3 3.631121
HSFB4 0.183747 0.477089 0.053908 3 3.714745
AN3 0.093538 0.549865 0.056604 3 3.700007
FAR1 0.045655 0.032345 0.005391 3 3.083390
RVN 0.015400 0.145553 0.061995 3 3.222947
... ... ... ... ... ...
GBF1 0.000007 0.048518 0.010782 3 3.059306
ABF3 0.000007 0.051213 0.016173 3 3.067393
AT3G17100 0.000007 0.037736 0.053908 3 3.091651
HFR1 0.000007 0.056604 0.005391 3 3.062002
PHR1 0.000007 0.045822 0.008086 3 3.053916

99 rows × 5 columns

In [40]:
## Stele
celltype1='per'
celltype2='pro'
celltype3='xyl'
celltype4='phl'
ts = tf_spec[tf_spec['tf_occurance']==4][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype4+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype4+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality', celltype4+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==12].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[40]:
per_betweenness_centrality pro_betweenness_centrality xyl_betweenness_centrality phl_betweenness_centrality per_out_centrality pro_out_centrality xyl_out_centrality phl_out_centrality per_in_centrality pro_in_centrality xyl_in_centrality phl_in_centrality centrality_count centrality_sum
AT3G43430 0.786389 0.797947 0.000011 0.229851 0.447073 0.367698 0.144089 0.856130 0.788294 0.930126 0.118227 0.924012 12 18.389846
MYB20 0.720003 0.828776 0.001622 0.122188 0.287671 0.710195 0.152709 0.255319 0.464508 0.785796 0.162562 0.055724 12 16.547073
DAG1 0.359877 0.058652 0.000027 0.017665 0.211706 0.328751 0.082512 0.544073 0.293898 0.738832 0.025862 0.621074 12 15.282929
HB40 0.231353 0.602280 0.234036 0.076906 0.376090 0.065292 0.167488 0.189463 0.042341 0.032073 0.133005 0.720365 12 14.870692
AT1G64620 0.312489 0.003954 0.830570 0.000075 0.325031 0.066438 0.052956 0.059777 0.093400 0.014891 0.039409 0.048632 12 13.847622
TCP14 0.467702 0.052274 0.001479 0.000181 0.077210 0.192440 0.131773 0.238095 0.024907 0.321879 0.087438 0.173252 12 13.768631
SGR5 0.000003 0.952535 0.000079 0.000020 0.070984 0.142039 0.131773 0.021277 0.009963 0.130584 0.065271 0.187437 12 13.711964
AT2G29660 0.016720 0.020035 0.705398 0.000012 0.102117 0.169530 0.141626 0.151976 0.048568 0.122566 0.110837 0.111449 12 13.700834
AT5G50010 0.704723 0.000914 0.001192 0.000010 0.042341 0.156930 0.211823 0.131712 0.148194 0.140893 0.115764 0.037487 12 13.691985
AT1G69580 0.004548 0.000422 0.000012 0.000187 0.029888 0.154639 0.094828 0.387031 0.141968 0.290951 0.084975 0.451874 12 13.641323
ATAUX2-11 0.396922 0.088761 0.016549 0.035209 0.099626 0.221077 0.121921 0.075988 0.044832 0.301260 0.181034 0.043566 12 13.626746
AT5G53420 0.559245 0.000615 0.002291 0.000040 0.171856 0.203895 0.006158 0.116515 0.336239 0.116838 0.009852 0.078014 12 13.601558
IAA13 0.130517 0.000406 0.009453 0.000053 0.038605 0.143184 0.108374 0.313070 0.119552 0.320733 0.087438 0.312057 12 13.583443
IAA26 0.003647 0.022702 0.000544 0.000162 0.085928 0.049255 0.187192 0.243161 0.036115 0.203895 0.137931 0.410334 12 13.380866
IAA11 0.126966 0.068010 0.000002 0.000139 0.018680 0.022910 0.006158 0.158055 0.191781 0.115693 0.078818 0.264438 12 13.051647
AT1G12440 0.061058 0.002543 0.018991 0.000059 0.084682 0.306987 0.070197 0.142857 0.026152 0.155785 0.061576 0.061803 12 12.992691
NF-YC9 0.000011 0.000512 0.000046 0.000001 0.029888 0.132875 0.020936 0.093212 0.038605 0.032073 0.022167 0.088146 12 12.458472
In [41]:
## Pericycle
celltype = 'per'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[41]:
per_betweenness_centrality per_out_centrality per_in_centrality centrality_count centrality_sum
AT3G21330 0.821422 0.254047 0.087173 3 4.162643
LBD14 0.768766 0.251557 0.191781 3 4.212104
GATA23 0.710124 0.053549 0.146949 3 3.910622
AT2G20100 0.578906 0.240349 0.134496 3 3.953750
IDD11 0.555840 0.053549 0.214197 3 3.823585
AT2G14880 0.445789 0.085928 0.093400 3 3.625117
MGP 0.435397 0.371108 0.099626 3 3.906131
AT2G35430 0.090613 0.026152 0.048568 3 3.165332
SAP 0.085366 0.186800 0.031133 3 3.303298
ICE1 0.002576 0.049813 0.170610 3 3.222999
FRS9 0.001966 0.006227 0.002491 3 3.010683
AT3G60580 0.001957 0.067248 0.053549 3 3.122754
GAI 0.001360 0.042341 0.049813 3 3.093515
HB21 0.001242 0.002491 0.063512 3 3.067245
ZFP7 0.001242 0.044832 0.085928 3 3.132002
AT3G11450 0.000731 0.056040 0.017435 3 3.074206
EMB1674 0.000674 0.012453 0.012453 3 3.025581
MYBR1 0.000410 0.027397 0.054795 3 3.082602
AT3G20640 0.000025 0.044832 0.052304 3 3.097161
In [42]:
## Procambium
celltype = 'pro'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[42]:
pro_betweenness_centrality pro_out_centrality pro_in_centrality centrality_count centrality_sum
ERF12 0.882887 0.053837 0.183276 3 4.120000
AT1G75490 0.872234 0.011455 0.079038 3 3.962726
AT4G20970 0.811594 0.098511 0.004582 3 3.914687
GRF9 0.784993 0.012600 0.006873 3 3.804467
HAT9 0.732225 0.044674 0.255441 3 4.032340
ABO3 0.424150 0.098511 0.620848 3 4.143509
MYB6 0.036403 0.072165 0.140893 3 3.249461
MYB10 0.032328 0.027491 0.026346 3 3.086165
RVE1 0.025856 0.058419 0.238259 3 3.322534
SHY2 0.006908 0.058419 0.057274 3 3.122601
HB18 0.003434 0.049255 0.076747 3 3.129436
OFP7 0.002778 0.008018 0.049255 3 3.060052
AT5G61590 0.002282 0.026346 0.030928 3 3.059556
bZIP19 0.000846 0.100802 0.040092 3 3.141739
RAV2 0.000050 0.105384 0.168385 3 3.273819
AT3G16940 0.000032 0.124857 0.112257 3 3.237145
AT3G42860 0.000026 0.018328 0.025200 3 3.043554
NTL9 0.000024 0.043528 0.034364 3 3.077916
AT2G18090 0.000009 0.049255 0.027491 3 3.076756
NAC080 0.000004 0.022910 0.113402 3 3.136316
CRF12 0.000001 0.059565 0.032073 3 3.091639
TEM1 0.000001 0.021764 0.139748 3 3.161513
In [43]:
## Xylem
celltype = 'xyl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[43]:
xyl_betweenness_centrality xyl_out_centrality xyl_in_centrality centrality_count centrality_sum
HB31 0.805546 0.065271 0.056650 3 3.927467
ZHD3 0.768447 0.018473 0.082512 3 3.869432
VND4 0.767269 0.113300 0.224138 3 4.104707
VND7 0.719611 0.147783 0.215517 3 4.082912
VND3 0.656228 0.519704 0.572660 3 4.748592
VND5 0.604598 0.113300 0.208128 3 3.926026
MYB99 0.568006 0.018473 0.075123 3 3.661602
AT1G68810 0.566355 0.995074 0.969212 3 5.530641
ASL9 0.447735 0.099754 0.083744 3 3.631233
AT4G19630 0.313737 0.008621 0.001232 3 3.323589
FBH1 0.260037 0.022167 0.052956 3 3.335161
MYB46 0.251318 0.100985 0.227833 3 3.580136
HAT14 0.226949 0.459360 0.395320 3 4.081629
IAA31 0.217646 0.830049 0.657635 3 4.705331
DOF1 0.182107 0.219212 0.184729 3 3.586047
AT1G68200 0.141986 0.185961 0.096059 3 3.424005
VND2 0.096231 0.667488 0.711823 3 4.475541
AT3G22100 0.065099 0.022167 0.020936 3 3.108203
AGL58 0.059538 0.126847 0.109606 3 3.295992
XND1 0.039254 0.296798 0.328818 3 3.664870
MYB83 0.022333 0.102217 0.166256 3 3.290806
AP3 0.022133 0.498768 0.358374 3 3.879275
ETC1 0.021510 0.215517 0.195813 3 3.432840
LBD18 0.019457 0.002463 0.100985 3 3.122905
IAA6 0.018883 0.257389 0.282020 3 3.558292
BZIP49 0.016270 0.034483 0.030788 3 3.081540
AT4G16610 0.012504 0.039409 0.067734 3 3.119646
VND1 0.010294 0.150246 0.203202 3 3.363742
AT5G03510 0.007341 0.002463 0.091133 3 3.100937
AT5G18090 0.006981 0.009852 0.033251 3 3.050084
SPL2 0.006024 0.008621 0.025862 3 3.040507
TLP2 0.005612 0.102217 0.075123 3 3.182952
AT1G04850 0.004334 0.025862 0.022167 3 3.052363
MYB25 0.003681 0.007389 0.035714 3 3.046784
AT3G16350 0.002463 0.001232 0.043103 3 3.046798
AT1G51200 0.001529 0.039409 0.034483 3 3.075421
NST1 0.000203 0.108374 0.072660 3 3.181238
TCP20 0.000178 0.157635 0.120690 3 3.278503
HB17 0.000167 0.050493 0.062808 3 3.113468
TCP10 0.000085 0.077586 0.094828 3 3.172499
AT3G19080 0.000049 0.056650 0.064039 3 3.120738
MYB50 0.000009 0.104680 0.108374 3 3.213063
MYB60 0.000005 0.086207 0.102217 3 3.188428
MAF1 0.000005 0.016010 0.020936 3 3.036950
AT5G06770 0.000005 0.034483 0.020936 3 3.055423
AT1G26610 0.000005 0.102217 0.094828 3 3.197049
HAG5 0.000002 0.034483 0.023399 3 3.057883
ASHH3 0.000002 0.033251 0.020936 3 3.054189
AT4G25210 0.000002 0.032020 0.029557 3 3.061578
In [44]:
## Phloem
celltype = 'phl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[44]:
phl_betweenness_centrality phl_out_centrality phl_in_centrality centrality_count centrality_sum
AT1G26790 0.760264 0.067882 0.094225 3 3.922371
NAC020 0.144219 0.144883 0.544073 3 3.833175
AT2G03500 0.120619 0.129686 0.857143 3 4.107448
AT5G02460 0.068212 0.133739 0.270517 3 3.472468
REM22 0.059641 0.052685 0.112462 3 3.224788
AT5G41380 0.058973 0.103343 0.310030 3 3.472346
CRF1 0.024421 0.054711 0.179331 3 3.258463
DAR2 0.010889 0.161094 0.835866 3 4.007850
GATA20 0.010747 0.090172 0.269504 3 3.370423
NAC057 0.009110 0.198582 0.621074 3 3.828766
AT3G12730 0.008489 0.215805 0.881459 3 4.105753
KAN2 0.003750 0.106383 0.191489 3 3.301622
HCA2 0.002340 0.159068 0.573455 3 3.734863
NAC2 0.001369 0.112462 0.217832 3 3.331663
AGL15 0.000946 0.073961 0.156028 3 3.230936
VOZ1 0.000846 0.122594 0.187437 3 3.310876
WRKY32 0.000606 0.119554 0.192503 3 3.312663
SVP 0.000260 0.077001 0.188450 3 3.265711
IDD16 0.000176 0.063830 0.082067 3 3.146072
WRKY39 0.000157 0.086120 0.085106 3 3.171383
SCL27 0.000065 0.033435 0.045593 3 3.079092
HMGB1 0.000062 0.050659 0.096251 3 3.146971
SIGC 0.000055 0.050659 0.049645 3 3.100359
ARF4 0.000053 0.112462 0.101317 3 3.213833
NF-YC3 0.000044 0.085106 0.117528 3 3.202678
bZIP2 0.000041 0.067882 0.067882 3 3.135806
AT2G40200 0.000030 0.078014 0.009119 3 3.087163
RAP2.7 0.000030 0.044580 0.067882 3 3.112492
AT1G26590 0.000022 0.015198 0.011145 3 3.026364
WOX2 0.000021 0.019250 0.036474 3 3.055745
IAA10 0.000017 0.032421 0.030395 3 3.062834
RFI2 0.000017 0.017224 0.010132 3 3.027373
EDA16 0.000013 0.062817 0.059777 3 3.122607
ET2 0.000012 0.086120 0.086120 3 3.172251
WRKY57 0.000009 0.016211 0.042553 3 3.058773
RTV1 0.000007 0.062817 0.034448 3 3.097272
TLP10 0.000007 0.040527 0.036474 3 3.077008
AT1G63840 0.000006 0.087133 0.054711 3 3.141850
AT1G64530 0.000006 0.043566 0.093212 3 3.136784
AT1G61730 0.000004 0.080041 0.064843 3 3.144888
ARF17 0.000003 0.033435 0.025329 3 3.058767
SUF4 0.000003 0.015198 0.007092 3 3.022293
AT3G51120 0.000003 0.025329 0.006079 3 3.031411
RR2 0.000003 0.023303 0.057751 3 3.081057
AT1G02030 0.000002 0.035461 0.025329 3 3.060792
CDF2 0.000002 0.063830 0.028369 3 3.092201
NF-YB8 0.000002 0.071935 0.041540 3 3.113477
ING2 0.000001 0.014184 0.011145 3 3.025330
FBH3 0.000001 0.029382 0.021277 3 3.050660
AGL80 0.000001 0.035461 0.020263 3 3.055725
MYB55 0.000001 0.088146 0.069909 3 3.158056
NAC089 0.000001 0.022290 0.001013 3 3.023304
AT5G28040 0.000001 0.022290 0.030395 3 3.052686
AT5G63700 0.000001 0.058764 0.187437 3 3.246202
NF-YB1 0.000001 0.031408 0.019250 3 3.050660
MBF1A 0.000001 0.021277 0.031408 3 3.052686

Search for individual genes¶

In [45]:
gene = 'SHR'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[45]:
tf_occurance end_degree_centrality end_out_centrality end_in_centrality end_betweenness_centrality end_closeness_centrality end_eigenvector_centrality per_degree_centrality per_out_centrality per_in_centrality ... xyl_out_centrality xyl_in_centrality xyl_betweenness_centrality xyl_closeness_centrality xyl_eigenvector_centrality phl_degree_centrality phl_out_centrality phl_in_centrality phl_closeness_centrality phl_eigenvector_centrality
SHR 3.0 0.086253 0.067385 0.018868 0.000036 0.000455 0.030447 0.473225 0.333748 0.139477 ... 0.083744 0.071429 0.004194 0.000282 0.063044 0.043566 0.042553 0.001013 0.000294 0.022988

1 rows × 29 columns

In [46]:
gene = 'BLJ'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[46]:
tf_occurance cor_degree_centrality cor_out_centrality cor_in_centrality cor_betweenness_centrality cor_closeness_centrality cor_eigenvector_centrality end_degree_centrality end_out_centrality end_in_centrality end_betweenness_centrality end_closeness_centrality end_eigenvector_centrality
BLJ 2.0 0.062147 0.019774 0.042373 0.040156 0.000313 0.025433 0.857143 0.086253 0.770889 0.362024 0.000513 0.123284
In [47]:
gene = 'JKD'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[47]:
tf_occurance cor_degree_centrality cor_out_centrality cor_in_centrality cor_closeness_centrality cor_eigenvector_centrality end_degree_centrality end_out_centrality end_in_centrality end_betweenness_centrality end_closeness_centrality end_eigenvector_centrality col_degree_centrality col_out_centrality col_in_centrality col_betweenness_centrality col_closeness_centrality col_eigenvector_centrality
JKD 2.0 0.610169 0.081921 0.528249 0.000405 0.140905 0.722372 0.291105 0.431267 0.00016 0.000606 0.112702 0.047399 0.028902 0.018497 0.71696 0.000286 0.016761
In [48]:
gene = 'RVN'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[48]:
tf_occurance cor_degree_centrality cor_out_centrality cor_in_centrality cor_closeness_centrality cor_eigenvector_centrality end_degree_centrality end_out_centrality end_in_centrality end_betweenness_centrality end_closeness_centrality end_eigenvector_centrality col_degree_centrality col_out_centrality col_in_centrality col_closeness_centrality col_eigenvector_centrality
RVN 1.0 0.112994 0.062147 0.050847 0.000328 0.052226 0.207547 0.145553 0.061995 0.0154 0.000535 0.056677 0.00578 0.003468 0.002312 0.000189 0.002151
In [49]:
gene = 'BIB'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[49]:
tf_occurance cor_degree_centrality cor_out_centrality cor_in_centrality cor_closeness_centrality cor_eigenvector_centrality end_degree_centrality end_out_centrality end_in_centrality end_betweenness_centrality end_closeness_centrality end_eigenvector_centrality
BIB 1.0 0.008475 0.00565 0.002825 0.00019 0.004724 0.390836 0.061995 0.328841 0.240286 0.000435 0.079241
In [50]:
gene = 'IME'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[50]:
In [51]:
gene = 'MYB66'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[51]:
In [52]:
gene = 'GL2'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[52]:
tf_occurance atri_degree_centrality atri_out_centrality atri_in_centrality atri_betweenness_centrality atri_closeness_centrality atri_eigenvector_centrality tri_degree_centrality tri_out_centrality tri_in_centrality tri_betweenness_centrality tri_closeness_centrality tri_eigenvector_centrality lrc_degree_centrality lrc_out_centrality lrc_in_centrality lrc_betweenness_centrality lrc_closeness_centrality lrc_eigenvector_centrality
GL2 3.0 0.760784 0.34902 0.411765 0.918236 0.001468 0.111362 0.703209 0.59492 0.108289 0.703726 0.001076 0.153611 0.051948 0.031877 0.020071 0.472882 0.000483 0.028321
In [53]:
tf_spec.to_csv('TF_GRN_centrality_t1-t3_zscore3.csv', index=True)
In [ ]: